About the job
Amazon Search is reinventing how customers find products through natural-language and semantic understanding. We are looking for an Applied Scientist II to push the science behind Natural Language Search that interprets complex, constraint-rich shopping queries, retrieves and ranks the most relevant products. You will build and ship large-scale relevance and ranking models that measurably reduce the rate at which customers see irrelevant results, working on problems that span query understanding, semantic matching, and contextual ranking at Amazon scale.
Responsibilities
Design, train, and ship deep-learning ranking and semantic-matching models that improve search relevance and reduce how often customers see irrelevant results, across hard query types.
Build the training data and evaluation methods that make these models work: synthetic and historical labels, hard-negative mining, and targeted sampling at the cases where search fails.
Develop signals that match product attributes to what the customer actually asked for.
Run offline and online A/B experiments, analyze precision/recall tradeoffs, and iterate to launch.
Work with engineers and scientists across teams to take models from prototype to production at Amazon scale.
Qualifications
Minimum
PhD, or Master's degree and 2+ years of CS, CE, ML or related field experience
Experience programming in Java, C++, Python or related language
Experience with one of the following areas: machine learning technologies, Reinforcement Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP) or related applications
Preferred
Experience in machine learning, data mining, information retrieval, statistics or natural language processing
1+ years of building large-scale machine-learning infrastructure for online recommendation, ads ranking, personalization or search experience
Experience with A/B testing
Experience in practical work applying ML to solve complex problems for large scale applications
Publications in ML, IR, or NLP venues (e.g., NeurIPS, ICML, SIGIR, KDD, ACL)
Experience training large-scale or deep neural ranking/relevance models
Experience taking ML models from prototype to production